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This is the AmeriFlux version of the carbon flux data for the site US-MN1 Morris: Corn-Soybean with Cover Crops, Strip Tillage. Site Description - Cropland, 2-year rotation (corn-soybean), strip tillage with winter cover crops (annual rye), nutrient management by soil tests, uniform pest management, tile drainage
This is the AmeriFlux version of the carbon flux data for the site US-MN2 Morris: Corn-Soybean-Wheat with Cover Crops, Minimal Tillage. Site Description - Cropland, 3-year rotation (corn-soybean-wheat), cover crops (winter camelina and pennycress), minimum tillage, nutrient management by soil tests, uniform pest management, tile drainage
This is the AmeriFlux version of the carbon flux data for the site US-MN3 Morris: Corn-Soybean, Conventional Tillage. Site Description - Cropland, 2-year rotation (corn-soybean), conventional (deep rip) tillage, uniform nutrient management, uniform pest management, tile drainage
Liquid slurries at high loadings (pumpable slurries) are critical to handle the streams between the unit operations in a biorefinery. Their liquefaction potential was initially tested with a small-scale method using 3FPU Ctec-2 /g dry biomass, and results were confirmed in 1L bioreactors experiments. Liquid slurries of cobs were observed in small scale tests (based on visual appearance, and reducing sugars released). Results were confirmed in 1L bioreactors under similar experimental conditions. Yield stress value in bioreactor was 34 ± 6Pa at 96 h, and glucan and xylan conversion to glucose and xylose, respectively, were 41 and 21%. In addition, work has been initiated with liquefaction of other pelleted corn stover fractions: Husk/Leaf (A),Husk (B), Stems (D) and controls (whole corn stover; F and G). Compositional analysis of the pellets showed glucan content (%) in the samples ranged from 31.9 to 36.1, xylan from 19.1 to 20.9, total lignin 13.1 to 18.8,and total ash 1.73 to 10.19. The moisture content ranged from 6.21 to 11.66%. Given these similar characteristics, we also examined liquefaction of these other samples, and they also showed that liquid slurries are possible. Work is continuing to define favourable conditions for liquid slurries formation.
Production of biofuels, bioproducts, and bioenergy requires a well-characterized, stable, and reasonably uniform biomass supply and well-established supply chains for shipping biomass from farm fields to biorefineries, while achieving year-round production targets. Preserving and stabilizing biomass feedstock during storage is a necessity for cost-effective and sustainable biofuel production. Ensiling is a common storage method used to preserve and even improve forage quality; however, the impact of ensiling on biomass physical and chemical properties that influence bioconversion processes has been variable. Our objective in this work was to determine the effects of ensiling on lignocellulosic feedstock physicochemical properties and how that influences bioconversion requirements. We observed statistically significant decreases (p < 0.05) in the content of two major structural carbohydrates (glucan and xylan) of 5 and 8%, respectively, between the ensiled and non-ensiled materials. We were unable to detect differences in sugar yields from structural carbohydrates after pretreatment and enzymatic hydrolysis of the ensiled materials compared to non-ensiled controls. Based on this work, we conclude that ensiling the corn stover did not change the bioconversion requirements compared to the control samples and incurred losses of structural carbohydrates. At the light microscopy level, ensiled corn stover exhibited little structural change or relocation of cell wall components as detected by immunocytochemistry. However, more subtle structural changes were revealed by electron microscopy, as ensiled cell walls exhibit ultrastructural characteristics such as wall delimitation intermediate between non-ensiled and dilute-acid-pretreated cell walls. These findings suggest that alternative methods of conversion, such as deacetylation and mechanical refining, could take advantage of lamellar defects and may be more effective than dilute acid or hot water pretreatment for biomass conversion of ensiled materials.
A novel biomass production system, integrating the co-harvesting and co-storage of moist corn grain and stover, promises a reduction in delivered feedstock costs. In this innovative method, the dry grain traditionally utilized for feed or biofuel production will now be processed at a considerably greater moisture content. The adoption of this approach may necessitate a substantial redesign of existing material handling infrastructure to effectively accommodate the handling and storage of moist grain after processing by milling or grinding. A comprehensive study was conducted to quantify the physical properties of this grain after processing with a knife processor or a hammermill. The geometric mean particle size, bulk and tapped density, sliding angle, material coefficient of friction, and discharged angle of repose were quantified. Five grain treatments, either fermented or unfermented, and having different moisture contents, were used. After processing, the moist, fermented ground grain exhibited a significantly smaller particle size compared to the dry grain. Additionally, both moist processed grains resulted in a decreased bulk density and increased material sliding angle, friction coefficient, and angle of repose. The examined metrics collectively suggest that handling, mixing, and storing moist ground grain will pose significant challenges compared to conventional dry ground grain. This increased difficulty may lead to substantially higher costs, a crucial factor that must be carefully considered when evaluating the overall economics of implementing this new biomass production system using combined harvesting and storage of corn grain and stover.
Objective: This study was to assess impacts of including 10% defatted microalgae (Nannochloropsis oceanica, 45% crude protein and 3.8% ether extract) into a corn-soybean meal basal diet for broiler chickens on apparent retention and ileal digestibility of nutrients. Procedures: Day-old hatchling Cornish Giant cockerels were divided into two groups (6 cages/group, 6 chicks/cage) and fed either the basal (control) or the microalgae-supplemented (microalgae) diet for 6 weeks. Starting week 3, the two experimental diets were mixed with 0.2% chromium oxide as an indigestible marker. Total excreta of individual cages were collected daily for 3 consecutive days during week 6. At the end of week 6, chicks were euthanized to collect ileal digesta from 1 chick/cage. Energy and concentrations of dry matter, crude protein, ether extract, minerals, and amino acids in digesta, excreta, and diets were assayed. Apparent retentions and ileal digestibilities were calculated using the direct collection and indirect indicator methods. Data were analyzed by the Student’s t-test and the significant level of difference was at p < 0.05. Results: The microalgae diet had elevated (4.2% and 8.1% by the two methods, p < 0.05) apparent retention of ether extract, but similar apparent retention and ileal digestibility of dry matter and apparent metabolizable energy, compared with the control diet. Supplemental defatted microalgae decreased (p < 0.05) the apparent retention and ileal digestibility of dietary crude protein by 13% and 13%. The supplementation also decreased (p < 0.05) ileal digestibilities and apparent retentions of multiple amino acids ranging from 0.6% to 26%. Apparent retentions of sodium and total phosphorus were decreased (p < 0.05) by 13% and 71%, respectively, by the microalgae inclusion compared with the control. Conclusion: The inclusion of 10% defatted N. oceanica in the corn-soybean meal basal diet for broiler chickens led moderate increases in apparent retention and ileal digestibility of ether extract, but decreased those of crude protein, amino acids, sodium, and phosphorus.
A comprehensive review of the literature shows that enzyme hydrolysis efficiency decreases with increased solids loadings at constant enzyme:cellulose ratios for pretreated lignocellulosic substrates. In seeking a mechanistic explanation for this phenomenon, we found that a nitrogen atmosphere enhances enzyme hydrolysis and minimizes the decrease in glucose yields as solids loadings are increased in an agitated bioreactor. Further, for liquid hot water pretreated corn stover, at solids loadings of both 100 and 200 g/L and hydrolyzed for 72 hr in a 1 L bioreactor at pH 5.0 with 3.6 mg protein per g biomass, glucose yields were 55% in a nitrogen atmosphere versus 45% in air with agitation and about 34% without agitation. While mixing promotes biomass/enzyme contact and disperses sugars released during hydrolysis that would otherwise cause product inhibition, nitrogen gas displaces air, avoiding deactivation of cellulases by oxygen. The nitrogen effect points to a facile approach of enhancing hydrolysis at high solids loadings.
Here, a lactic acid/chlorine chloride-based deep eutectic solvent (DES) was used for the extraction of high-purity lignin (up to 94.7%) in high yield (up to 75%) from the hydrolysis/fermentation residue corn stover hydrolysate (CSH), which was generated from a pilot-plant-scale biorefinery. A range of extraction conditions were investigated, which involved varying reaction temperature, time, and DES composition. The relationship between lignin yield, purity, and structural characteristics with DES treatment conditions was determined. The extraction of high-purity lignin from hydrolysis/fermentation residues presents a promising approach for enhancing the economic feasibility of a lignocellulose biorefinery. It was also determined that DES extraction can produce lignin with a controlled range of molecular weight and functional group content.
High variabilities of material attributes in lignocellulosic biomass present risks for biofuel and biochemical productions and must be mitigated via preprocessing. Since almost no mechanical device is originally designed for processing biomass, how to operate existing apparatuses with efficient performance has not been investigated extensively. This work presents a study on an integrated screening and air classification to separate cobs and stalks from husks and leaves in corn stover. Prototype machine learning models were developed to assess the feasibility of predicting the process outcome based on the measurable parameters. The models trained upon limited experimental data rendered decent predictive accuracy of yield and purity. The experimental data and modeling results collectively suggest decreasing throughput leads to a higher purity. To the contrary, if throughput increases, a lower purity is likely. A possible trade-off between yield and purity of the separated streams indicates the need for optimal combinations of feedstock size, moisture, and throughput to achieve optimized separations. The results of this study also suggest the need to further improve model predictability by developing more accurate formulations for physics governing the integrated unit operations. To accomplish this, additional experimental data needs to be generated for model training.
Sustainable fuel initiatives in the United States such as the Environmental Protection Agency’s Renewable Fuel Stan- dard and the Department of Energy’s Sustainable Aviation Fuel Grand Challenge have increased the production of corn ethanol and soybean biodiesel. However, the lack of precise information regarding biomass sourcing at a localized level has hindered accurate understanding of both biofuel costs and environmental impact of these production pathways. By harnessing the power of geospatial analysis and leveraging United States Department of Agriculture (USDA) crop cen- sus data, this dataset fills this critical knowledge gap. This dataset offers a novel estimation of geospatial biomass sourc- ing for biofuel production in the United States by synthe- sizing 2017 USDA crop census data, biorefinery data from the United States Energy Information Administration, and publicly available information about biomass sourcing for biofuel production. This dataset provides a detailed under- standing of biomass use for first generation biofuel pro- duction, enabling stakeholders to make informed decisions about resource allocation, investment strategies, and infras- tructure development. Furthermore, the county-level gran- ularity of the dataset allows for increased fidelity in the techno-economic assessments and life-cycle analyses of first- generation biofuels in the United States.
Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates is largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields (> 250 m). Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000-2020 across a 12 state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015-2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forests model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008-2014. We also evaluated the full 2000-2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R 2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R 2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000-2020, each standard deviation increase in rainfall delayed planting by ~ 2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.
Bicycloalkanes stand out as a replacement for aromatics from conventional jet fuel (CJF) because of their high energy density and lower freezing point. Most reports to date have focused on synthesizing bicycloalkanes using model compounds or single sugar components of lignocellulose. Here, we utilize all lignocellulosic sugars of corn stover (CS) to produce bicycloalkanes. First, furfural (FFR) and 5-hydroxymethyl furfural (5-HMF) are produced via acid-catalyzed dehydration, achieving combined FFRs molar yields ranging from 65% to 73%, respectively. The resulting FFRs are converted to cyclopentanones with molar yields within the range of 57%–63% via hydrogenation and Piancatelli rearrangement. Further aldol condensation of cyclopentanones yields C 10 -C 12 oxygenates with 80 mol %. Lastly, these oxygenates are directly hydrodeoxygenated to bicycloalkanes in a yield of 83.8 mol %. The resulting bicycloalkanes exhibit tier α fuel properties consistent with Jet A fuel (CJF) specifications and blend compatibility with CJF at a 37% volumetric ratio.
The high productivity in the US Corn Belt is largely enabled by the consumption of millions of tons of manufactured fertilizer. Excessive application of nitrogen (N) fertilizer has been pervasive in this region, and the unrecovered N eventually escaped from croplands in forms of nitrous oxide (N 2 O) emission and N leaching. Mitigating these negative impacts is hindered by a lack of practical information on where to focus and how much mitigation potential to expect. At a large scale, process-based crop models are the primary tools for predicting variables required by decision making, but their applications are prohibited by expensive computational and data storage costs. To overcome these challenges, we built a series of metamodels to learn the key mechanisms regarding the carbon (C) and N cycle from a well-validated process-based biogeochemical model, ecosys. The trained metamodel captures over 98% of the variability of the ecosys simulated outputs for 99 randomly selected counties in Iowa, Illinois, and Indiana. To identify hotspots with high mitigation potential, we introduce net societal benefit (NSB) as an indicator for synthesizing the loss in yield and social benefits through emissions and pollutants avoided. Our results show that reducing N fertilizer by 10% leads to 9.8% less N 2 O emissions and 9.6% less N leaching at the cost of 4.9% more SOC depletion and 0.6% yield reduction over the study region. The estimated total annual NSB is $\$395$ M (uncertainty ranges from $\$114$ M to $\$1271$ M), including $\$334$ from social benefits (uncertainty ranges from $\$46$ M to $\$1076$ M), $\$100$ M from saving fertilizer (uncertainty ranges from $\$13$ M to $\$455$ M), and –$\$40$ M due to yield changes (uncertainty ranges from –$\$261$ M to $\$69$ M). For the median scenario, we noted that 20% of the study area accounts for nearly 50% of the NSB, and thus represent hotspot locations for targeted mitigation. Although the uncertainty range suggests that developing such a high-resolution framework is not yet settled and the scenario based estimations are not appropriate to inform the management practices for individual farmers, our efforts shed light on the new generation of analytical tools for life cycle assessment.
Conservation tillage can reduce soil erosion, increase soil health, and decrease labor and fuel input costs. Despite these benefits, potential yield impacts remain an important concern for farmers considering adoption. Previous research suggests that conservation tillage is likely to have the largest yield benefits in more arid conditions, but a lack of field-level analyses across climatic, management and soil conditions limits confidence in such predictions. Satellite imagery provides the opportunity to monitor agricultural lands at sub-field resolution across large spatial scales and wide environmental gradients. Here we investigate the maize yield impacts of conservation tillage in the semi-arid western US Corn Belt, using sub-field resolution datasets on tillage practices and crop yields derived from satellite data spanning four states (Nebraska, Kansas, South Dakota, and North Dakota) between 2008 and 2020. On these datasets, we estimate heterogenous yield outcomes for several thousand maize fields across gradients in climate, soil quality and irrigation status by using a causal forests analysis, an adaptation of the random forests machine-learning algorithm for causal inference on observational data. We find that long-term adoption of conservation tillage increased rainfed maize yields by an average of 9.9% in the region. Impacts on irrigated yields were small and not statistically significant. These results, along with an analysis of variables related to greater than average yield benefits, indicate that improved water infiltration and retention are the primary reasons for conservation tillage benefits. Despite yield benefits, many fields estimated to see increased yields under long term low till have not adopted the practice. Therefore, we identify specific counties likely to benefit most from increased levels of adoption. Our results strengthen the understanding of the impacts of conservation agriculture on crop yields and help define environments and counties most likely to benefit from conservation tillage.
Abstract Feedstock variability represents a challenge in lignocellulosic biorefineries, as it can influence both lignocellulose deconstruction and microbial conversion processes for biofuels and biochemicals production. The impact of feedstock variability on microbial performance remains underexplored, and predictive tools for microbial behaviour are needed to mitigate risks in biorefinery scale‐up. Here, twelve batches of corn stover were deconstructed via deacetylation, mechanical refining, and enzymatic hydrolysis to generate lignin‐rich and sugar streams. These batches and their derived streams were characterised to identify their chemical components, and the streams were used as substrates for producing muconate and butyrate by engineered Pseudomonas putida and wildtype Clostridium tyrobutyricum , respectively. Bacterial performance (growth, product titers, yields, and productivities) differed among the batches, but no strong correlations were identified between feedstock composition and performance. To provide metabolic insights into the origin of these differences, we evaluated the effect of twenty‐three isolated chemical components on these microbes, including three components in relevant bioprocess settings in bioreactors, and we found that growth‐inhibitory concentrations were outside the ranges observed in the streams. Overall, this study generates a foundational dataset on P. putida and C. tyrobutyricum performance to enable future predictive models and underscores their resilience in effectively converting fluctuating lignocellulose‐derived streams into bioproducts.
Lignin has been shown to be a recalcitrance factor in many biomass conversion studies. To better understand the effects of lignin on cellulose conversion, different lignin fractions were extracted from the same dilute acid pretreated corn stover by three sequential isolation methods, namely ethanol extraction, dioxane extraction, and enzyme purification. The physicochemical properties of each lignin fraction including molecular weight distribution, surface area, surface charge, and other structural features varied, depending on the isolation methods. All three lignin fractions had negative surface charges, and ethanol-extracted lignin carried the highest surface charges, followed by dioxane-extracted lignin and cellulase-purified residual lignin. These physicochemical properties of lignin fractions also resulted in different extent of inhibitory effects on enzymatic hydrolysis of microcrystalline cellulose (MCC). Dioxane-extracted lignin exhibited the highest inhibitory effect on glucose release from MCC, followed by the cellulase-purified residual lignin fraction and ethanol-extracted lignin. Furthermore, lignin fractions with higher contents of syringyl (S) substructure and β-O-4 aryl ether interunit linkages showed a stronger negative effect on cellulase hydrolysis of MCC.
The goal of this Case Study was to quantify the impacts of variable moisture and ash on hammer mill throughput and energy consumption and on loss of very wet stover that causes failures in the first stage grinder and that are not able to be fed to conversion, as compared to a status quo Base Case system. Also considered was convertible carbohydrate content (minimum total carbohydrate specification) and maximum ash content and the delivered feedstock cost impacts of not being able to feed stover not meeting the total carbohydrate specification to the conversion reactor. Laboratory data on the impacts of moisture content and tissue fraction on throughput and energy consumption in a stage 2 hammer mill were received from FCIC Subtask 5.1. Additional air classifier throughput, energy consumption and separation efficiency data were obtained from FCIC Subtask 5.1 for the new air classifier, which has three exit streams (lights, middle and heavies). These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Because the ash contents and particle sizes had not been analyzed in the laboratory at the time of the model runs, we assumed that the ash distributed proportionally with total mass into the lights and heavies in an air classifier having two exit streams (lights and heavies) and that the lights fraction from the air classifier was not removed. Key takeaways from this Case Study are that due to lower energy consumption, it is more cost effective to hammer mill fractionated corn stover tissues than whole stover. Reduction of grinding energy was significant and may possibly be connected to particle-particle interactions in the grinder that lead to increased residence time of leaves and husks, resulting in decreased throughput and higher generation of fines when milling whole stover. While we did not see significant impacts to throughput, this was due to moisture failures of the first stage grinder in each system dominating failures and downtime. The operating cost savings of reduced grinding energy savings in the second stage hammer mills alone was high enough to offset the added capital cost of the air classifier and extra grinding line.